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Cognitive Internet of Medical Things for Smart Healthcare

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Cover of 'Cognitive Internet of Medical Things for Smart Healthcare'

Table of Contents

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    Book Overview
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    Chapter 1 A Review of Applications, Security and Challenges of Internet of Medical Things
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    Chapter 2 IoT Enabled Technology in Secured Healthcare: Applications, Challenges and Future Directions
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    Chapter 3 A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications
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    Chapter 4 Applications and Challenges of Cloud Integrated IoMT
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    Chapter 5 Optimal SVM Based Brain Tumor MRI Image Classification in Cloud Internet of Medical Things
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    Chapter 6 An Effective Fuzzy Logic Based Clustering Scheme for Edge-Computing Based Internet of Medical Things Systems
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    Chapter 7 Automated Internet of Medical Things (IoMT) Based Healthcare Monitoring System
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    Chapter 8 Deep Belief Network Based Healthcare Monitoring System in IoMT
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    Chapter 9 An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques
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    Chapter 10 QoS Optimization in Internet of Medical Things for Sustainable Management
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    Chapter 11 An Intelligent Internet of Medical Things with Deep Learning Based Automated Breast Cancer Detection and Classification Model
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    Chapter 12 Internet of Medical Things (IoMT) Enabled Skin Lesion Detection and Classification Using Optimal Segmentation and Restricted Boltzmann Machines
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    Chapter 13 An IOT Based Medical Tracking System (IMTS) and Prediction with Probability of Infection
Attention for Chapter 4: Applications and Challenges of Cloud Integrated IoMT
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Citations

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Chapter title
Applications and Challenges of Cloud Integrated IoMT
Chapter number 4
Book title
Cognitive Internet of Medical Things for Smart Healthcare
Published by
Springer, Cham, January 2021
DOI 10.1007/978-3-030-55833-8_4
Book ISBNs
978-3-03-055832-1, 978-3-03-055833-8
Authors

Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal, Pinto Kumar Paul, Bharati, Subrato, Podder, Prajoy, Mondal, M. Rubaiyat Hossain, Paul, Pinto Kumar

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 13%
Student > Ph. D. Student 5 13%
Student > Bachelor 2 5%
Other 2 5%
Lecturer 1 3%
Other 3 8%
Unknown 21 54%
Readers by discipline Count As %
Computer Science 8 21%
Engineering 5 13%
Business, Management and Accounting 1 3%
Chemical Engineering 1 3%
Social Sciences 1 3%
Other 1 3%
Unknown 22 56%